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Record W2286492932 · doi:10.1002/cam4.559

Comparison of outcome of patients with CLL who are referred or nonreferred to a specialized <scp>CLL</scp> clinic: a Canadian population‐based study

2016· article· en· W2286492932 on OpenAlexafffundabout
Sara Beiggi, Versha Banerji, Angela Deneka, Jane Griffith, Spencer B. Gibson, James B. Johnston

Bibliographic record

VenueCancer Medicine · 2016
Typearticle
Languageen
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsUniversity of ManitobaCancerCare Manitoba
FundersCancerCare Manitoba FoundationRoche
KeywordsMedicineReferralChronic lymphocytic leukemiaPopulationInternal medicineChemotherapyCancerPediatricsLeukemiaFamily medicine

Abstract

fetched live from OpenAlex

Chronic lymphocytic leukemia and small lymphocytic lymphoma (CLL/SLL) patients in Manitoba are either referred to the CLL Clinic at CancerCare Manitoba (CCMB) or are followed by other hematologists and general practitioners. However, it has been unclear whether referral to the CLL clinic influences patient outcome. Overall survival (OS) was assessed for all CLL/SLL patients diagnosed in Manitoba between 2007 and 2011. Of 555 patients, 281 (51%) were referred to the CLL clinic. Patients seen in this clinic had a twofold increased OS compared to patients who were managed by other hematologists and general practitioners (HR 2.375, P 0.0002) when adjusted for age, gender, presence of pre- or post-CLL cancer, treatment and urban/rural location. In the nonreferred population there was a striking correlation between advancing age and decreasing OS. However, this correlation was almost eliminated in the referred population who were more likely to receive chemotherapy. Patients referred and seen in the CLL clinic have an improved OS compared to nonreferred patients and this appears to be primarily related to improved OS in the elderly. Possible explanations for this finding are discussed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.135
GPT teacher head0.443
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations13
Published2016
Admission routes3
Has abstractyes

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